Ad hoc wireless Geosensor Networks (GSN) Introduction Position determination and usage Integration in spatial data infrastructures

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1 Ad hoc wireless Geosensor Networks (GSN) Introduction Position determination and usage Integration in spatial data infrastructures Prof. Dr.-Ing. Ralf Bill Rostock University Faculty for Agricultural and Environmental Sciences Professorship for Geodesy and Geoinformatics

2 Geodesy and Geoinformatics Founded as institute in April Interdisciplinary team of Geodesists, Geoinformaticians, Computer scientists, Geographers, Cartographers and Dipl.-Ing. for Land Cultivation and Environmental Protection. In average around 20 collaborators (thereby 8 funded by the University). Geodesy - Surveying ubiquituous Geoinformatics GG_DFG_GSN UNIVERSITÄT ROSTOCK, PROFESSUR FÜR GEODÄSIE UND GEOINFORMATIK 2

3 Basic idea: Arbitrarely distribution of thousands of sensor nodes in an area Spontaneous linkage of the nodes Capturing physical parameters Transfer to data sink (PC, Server) Task of each sensor node: Determine measurement values Simple data processing Communication/data transfer to neighboring nodes Optimal energy usage Properties of a sensor network: Self configuration/self healing/self control Cooperative May be mobile Heavy data traffic to the sink Ad hoc wireless Geosensor networks (GSN) Gateway Communication unit Calculating unit Battery Satellite Sensor network Sensors Internet Sink GG_DFG_GSN UNIVERSITÄT ROSTOCK, PROFESSUR FÜR GEODÄSIE UND GEOINFORMATIK 3

4 Philosophy A single sensor is useless. The cooperation of hundreds or thousands of sensors creates usable results. Preconditions Sensor nodes are randomly deployed Position/Location initially unknown Why do we need location/positions? Position Measurement Geographic Routing Clustering strategies Changing topology and mobility Hindrance detection Functionality in a GIS Problem Statement GG_DFG_GSN UNIVERSITÄT ROSTOCK, PROFESSUR FÜR GEODÄSIE UND GEOINFORMATIK 5

5 Error Precise Localization in GSN Research project GeoSens/GeoSens2: Adaptation of geodetic adjustment procedures to energy limited geosensor networks Funding: DFG Duration: / Joint project with: Institute for Applied Microelectronics and Computer Engineering, Uni Rostock, Prof. Timmermann Developed algorithms so far: Staff: DLS, idls, mdls, sdls RAL CL, WCL, AWCL Alexander Born (GG) Frank Reichenbach/Ralf Behnke (IMD) Field: 100x100 Photo: Hitachi Y X Relative positioning error: 2,5 % GG_DFG_GSN UNIVERSITÄT ROSTOCK, PROFESSUR FÜR GEODÄSIE UND GEOINFORMATIK 6

6 Problem Solution: Distribution Some nodes (beacons) carry GNSS or other large positioning devices and do have no energy and computer limitations All other sensor nodes determine their position independently so called unknowns Estimation of the position based on measurements Distances Angles d 1 d 2 d 3 GG_DFG_GSN UNIVERSITÄT ROSTOCK, PROFESSUR FÜR GEODÄSIE UND GEOINFORMATIK 7

7 Accuracy/Error Goal Development of new adaptable localization methods for GSN Resource Aware Localization (RAL) Distributed Least Squares (DLS) Fine Grained Localization (FGL) Convex Position Estimation (CPE) APIT Weighted Centroid Localization (WCL) Coarse Grained Localization (CGL) Nearest Beacon (NB) Resources requirements for sensor nodes GG_DFG_GSN UNIVERSITÄT ROSTOCK, PROFESSUR FÜR GEODÄSIE UND GEOINFORMATIK 11

8 EEP Definition of the Energy-Error-Product EFP = E E Measurement for the quality of localization algorithms Energy-Error-Product (EEP) RAL DLS WCL CGL APIT FGL GG_DFG_GSN UNIVERSITÄT ROSTOCK, PROFESSUR FÜR GEODÄSIE UND GEOINFORMATIK 12

9 Observations Research fields Current observations are in most cases not precise (RSSI etc.), from RSSI to time-based methods Which type of observations (distances, angles, positions.. RFID, UWB..)? Methods Geodetic methods => positioning with radio signals => rough estimations (nearest beacon) From coarse-grained to fine-grained methods (Adjustment of over-determined systems) Integration of conditions in position determination: geometric dependencies, a priori information Hybrid localization by combination of active sensor nodes and passive RFID, by integrating GNSS In static and kinematic status In resource poor and resource rich environments Outlier tests (Least squares, Least median square, Maximum Subsample) Accuracy analysis of positioning approaches Network optimisations GG_DFG_GSN UNIVERSITÄT ROSTOCK, PROFESSUR FÜR GEODÄSIE UND GEOINFORMATIK 13

10 Position as measurement - research topics Hindrance detection Cluster creation and positioning optimisation with respect to resources Research fields - Cluster node takes care for complex calculations (positioning, evaluation and communication) Embedding positions in Spatial data infrastructures => SLEWS Geosensor network = GIS Mobility aspects - Online data analysis and decision making in the network - Cluster detection (static, kinematic), movement patterns detection Monitoring: Movement analysis Navigation/Tracking: Cars, people, goods.. Use of Bayes Filter Higher elasticity (overcoming temporary disturbances) by distributed localization, hindrance detection and compensation using biologically inspired methods GG_DFG_GSN UNIVERSITÄT ROSTOCK, PROFESSUR FÜR GEODÄSIE UND GEOINFORMATIK 14

11 SLEWS Sensor-based landslide early warning systems (SLEWS) Geodetic and geoinformatics aspects in webbased early warning systems by integration of real-time sensors Funding: BMBF Duration: Cooperation with: RWTH Aachen BGR Hannover ScatterWeb Berlin Staff: Kai Walter Frank Niemeyer GG_DFG_GSN UNIVERSITÄT ROSTOCK, PROFESSUR FÜR GEODÄSIE UND GEOINFORMATIK 15

12 GSN in SLEWS Radio signal based communication (WSN) Low-cost sensors: Temperature, moisture, distance changes, inklinometer, accelerometer GNSS/Tacheometry for geodetic referencing and geodetic network for deformation analysis GG_DFG_GSN UNIVERSITÄT ROSTOCK, PROFESSUR FÜR GEODÄSIE UND GEOINFORMATIK 16

13 Information infrastructure Internet based information exchange using open standards independend from hardware and vendor Infrastructure modular set-up Exchangeable and scalable, cost efficient Interoperability to other external systems ensured Data Information Warning GG_DFG_GSN UNIVERSITÄT ROSTOCK, PROFESSUR FÜR GEODÄSIE UND GEOINFORMATIK 17

14 SensorWebEnablement Open Geospatial Consortium SWE-Initiative to describe and embed sensors in spatial data processing Specification Observations & Measurements Schema Sensor Model Language Transducer Markup Language Sensor Observations Service Sensor Planning Service Abbreviation O&M SensorML TML SOS SPS Sensor Alert Service/ Web Notification Services SAS/ WNS GG_DFG_GSN UNIVERSITÄT ROSTOCK, PROFESSUR FÜR GEODÄSIE UND GEOINFORMATIK 18

15 Early warning systems scenario GG_DFG_GSN UNIVERSITÄT ROSTOCK, PROFESSUR FÜR GEODÄSIE UND GEOINFORMATIK 19

16 Research fields Data Discovery & Access: Sensor discovery through spatial searches or intuitive terms and definitions (interconnection of search engines (e.g. Google), catalogue services (e.g. CSW) and sensor data/description, see data modeling) Combination of sensor web and location based services Auto-configuration methods for service client applications and information orchestration Public Awareness: Access to sensor information through popular Web 2.0 applications (e.g. SOS & Facebook (see 52 North), smart electricity meter & Twitter (see EnBW)) Crowdsourcing : Cell/Smartphones & People as sensors Data Services/Service chains and orchestration Sensor Fusion Enablement (information generation and decision support through combination of sensor web data and geospatial processing technologies, see OGC OWS-7 testbed) Usage of distributed processing capabilities (GRID, Application Engines, Cloud Computing) Improvement of ad hoc sensor integration into SDIs (auto configuration of interfaces between sensor and services tier) GG_DFG_GSN UNIVERSITÄT ROSTOCK, PROFESSUR FÜR GEODÄSIE UND GEOINFORMATIK 20

17 Research fields Sensor Hardware & Communication: Usage of increasing processing capabilities (e.g. energy efficient mobile processor architectures e.g. ARM or ATOM) Usage of increasing broadband internet availability (e.g. GSM-based 3.X G communication, nationwide broadband broadcast digital dividend ) Sensor system as self-contained web service provider Sensor system as GIS: Onboard processing vs. cloud/grid processing, combination of both Data Modelling: Enhancement of the Geo Web with Semantic Web technologies Organization of knowledge through use of dictionaries, ontologies for terms and definitions Harmonization of different (geospatial) data model standards Modeling of data from mobile sensor sources (see 52 North) Modeling of multi layered sensor web clusters GG_DFG_GSN UNIVERSITÄT ROSTOCK, PROFESSUR FÜR GEODÄSIE UND GEOINFORMATIK 21

18 Thanks to: Alexander Born Kai Walter Frank Reichenbach Frank Niemeyer Dirk Timmermann BMBF Geotechnologien Prof. Dr.-Ing. Ralf Bill Rostock University Rostock GG_DFG_GSN UNIVERSITÄT ROSTOCK, PROFESSUR FÜR GEODÄSIE UND GEOINFORMATIK 22

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